Program evaluation: An educator's portal into academic scholarship
Bibliographic record
Abstract
Program evaluation is an "essential responsibility" but is often not seen as a scholarly pursuit. While Boyer expanded what qualifies as educational scholarship, many still need to engage in processes that are rigorous and of a requisite academic standard to be labelled as scholarly. Many medical educators may feel that scholarly program evaluation is a daunting task due to the competing interests of curricular change, remediation, and clinical care. This paper explores how educators can take their questions around outcomes and efficacy of our programs and efficiently engage in education scholarship. The authors outline how educators can examine whether training programs have a desired impact and outcomes, and then how they might leverage this process into education scholarship.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.214 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".